## Running the Server

PrivateGPT supports running with different LLMs & setups.

### Local models

Both the LLM and the Embeddings model will run locally.

Make sure you have followed the *Local LLM requirements* section before moving on.

This command will start PrivateGPT using the `settings.yaml` (default profile) together with the `settings-local.yaml`
configuration files. By default, it will enable both the API and the Gradio UI. Run:

```bash
PGPT_PROFILES=local make run
```

or

```bash
PGPT_PROFILES=local poetry run python -m private_gpt
```

When the server is started it will print a log *Application startup complete*.
Navigate to http://localhost:8001 to use the Gradio UI or to http://localhost:8001/docs (API section) to try the API
using Swagger UI.

#### Customizing low level parameters

Currently, not all the parameters of `llama.cpp` and `llama-cpp-python` are available at PrivateGPT's `settings.yaml` file.
In case you need to customize parameters such as the number of layers loaded into the GPU, you might change
these at the `llm_component.py` file under the `private_gpt/components/llm/llm_component.py`.

##### Available LLM config options

The `llm` section of the settings allows for the following configurations:

- `mode`: how to run your llm
- `max_new_tokens`: this lets you configure the number of new tokens the LLM will generate and add to the context window (by default Llama.cpp uses `256`)

Example:

```yaml
llm:
  mode: local
  max_new_tokens: 256
```

If you are getting an out of memory error, you might also try a smaller model or stick to the proposed
recommended models, instead of custom tuning the parameters.

### Using OpenAI

If you cannot run a local model (because you don't have a GPU, for example) or for testing purposes, you may
decide to run PrivateGPT using OpenAI as the LLM and Embeddings model.

In order to do so, create a profile `settings-openai.yaml` with the following contents:

```yaml
llm:
  mode: openai

openai:
  api_base: <openai-api-base-url> # Defaults to https://api.openai.com/v1
  api_key: <your_openai_api_key>  # You could skip this configuration and use the OPENAI_API_KEY env var instead
  model: <openai_model_to_use> # Optional model to use. Default is "gpt-3.5-turbo"
                               # Note: Open AI Models are listed here: https://platform.openai.com/docs/models
```

And run PrivateGPT loading that profile you just created:

`PGPT_PROFILES=openai make run`

or

`PGPT_PROFILES=openai poetry run python -m private_gpt`

When the server is started it will print a log *Application startup complete*.
Navigate to http://localhost:8001 to use the Gradio UI or to http://localhost:8001/docs (API section) to try the API.
You'll notice the speed and quality of response is higher, given you are using OpenAI's servers for the heavy
computations.

### Using OpenAI compatible API

Many tools, including [LocalAI](https://localai.io/) and [vLLM](https://docs.vllm.ai/en/latest/),
support serving local models with an OpenAI compatible API. Even when overriding the `api_base`,
using the `openai` mode doesn't allow you to use custom models. Instead, you should use the `openailike` mode:

```yaml
llm:
  mode: openailike
```

This mode uses the same settings as the `openai` mode.

As an example, you can follow the [vLLM quickstart guide](https://docs.vllm.ai/en/latest/getting_started/quickstart.html#openai-compatible-server)
to run an OpenAI compatible server. Then, you can run PrivateGPT using the `settings-vllm.yaml` profile:

`PGPT_PROFILES=vllm make run`

### Using Azure OpenAI

If you cannot run a local model (because you don't have a GPU, for example) or for testing purposes, you may
decide to run PrivateGPT using Azure OpenAI as the LLM and Embeddings model.

In order to do so, create a profile `settings-azopenai.yaml` with the following contents:

```yaml
llm:
  mode: azopenai

embedding:
  mode: azopenai

azopenai:
  api_key: <your_azopenai_api_key>  # You could skip this configuration and use the AZ_OPENAI_API_KEY env var instead
  azure_endpoint: <your_azopenai_endpoint> # You could skip this configuration and use the AZ_OPENAI_ENDPOINT env var instead
  api_version: <api_version> # The API version to use. Default is "2023_05_15"
  embedding_deployment_name: <your_embedding_deployment_name> # You could skip this configuration and use the AZ_OPENAI_EMBEDDING_DEPLOYMENT_NAME env var instead
  embedding_model: <openai_embeddings_to_use> # Optional model to use. Default is "text-embedding-ada-002" 
  llm_deployment_name: <your_model_deployment_name> # You could skip this configuration and use the AZ_OPENAI_LLM_DEPLOYMENT_NAME env var instead
  llm_model: <openai_model_to_use> # Optional model to use. Default is "gpt-35-turbo"
```

And run PrivateGPT loading that profile you just created:

`PGPT_PROFILES=azopenai make run`

or

`PGPT_PROFILES=azopenai poetry run python -m private_gpt`

When the server is started it will print a log *Application startup complete*.
Navigate to http://localhost:8001 to use the Gradio UI or to http://localhost:8001/docs (API section) to try the API.
You'll notice the speed and quality of response is higher, given you are using Azure OpenAI's servers for the heavy
computations.

### Using AWS Sagemaker

For a fully private & performant setup, you can choose to have both your LLM and Embeddings model deployed using Sagemaker.

Note: how to deploy models on Sagemaker is out of the scope of this documentation.

In order to do so, create a profile `settings-sagemaker.yaml` with the following contents (remember to
update the values of the llm_endpoint_name and embedding_endpoint_name to yours):

```yaml
llm:
  mode: sagemaker

sagemaker:
  llm_endpoint_name: huggingface-pytorch-tgi-inference-2023-09-25-19-53-32-140
  embedding_endpoint_name: huggingface-pytorch-inference-2023-11-03-07-41-36-479
```

And run PrivateGPT loading that profile you just created:

`PGPT_PROFILES=sagemaker make run`

or

`PGPT_PROFILES=sagemaker poetry run python -m private_gpt`

When the server is started it will print a log *Application startup complete*.
Navigate to http://localhost:8001 to use the Gradio UI or to http://localhost:8001/docs (API section) to try the API.

### Using Ollama

Another option for a fully private setup is using [Ollama](https://ollama.ai/).

Note: how to deploy Ollama and pull models onto it is out of the scope of this documentation.

In order to do so, create a profile `settings-ollama.yaml` with the following contents:

```yaml
llm:
  mode: ollama

ollama:
  model: <ollama_model_to_use> # Required Model to use.
                               # Note: Ollama Models are listed here: https://ollama.ai/library
                               #       Be sure to pull the model to your Ollama server
  api_base: <ollama-api-base-url> # Defaults to http://localhost:11434
```

And run PrivateGPT loading that profile you just created:

`PGPT_PROFILES=ollama make run`

or

`PGPT_PROFILES=ollama poetry run python -m private_gpt`

When the server is started it will print a log *Application startup complete*.
Navigate to http://localhost:8001 to use the Gradio UI or to http://localhost:8001/docs (API section) to try the API.
